Login Register Cart 0
Generic placeholder image

Current Genomics

Editor-in-Chief

ISSN (Print): 1389-2029
ISSN (Online): 1875-5488

Back
Journal Home About Journal Editorial Board Journal Insight Current Issue Volumes/Issues
Subscribe
Research Article

MetaConClust - Unsupervised Binning of Metagenomics Data using Consensus Clustering

Author(s): Dipro Sinha, Anu Sharma*, Dwijesh Chandra Mishra, Anil Rai, Shashi Bhushan Lal, Sanjeev Kumar, Moh. Samir Farooqi and Krishna Kumar Chaturvedi

Volume 23, Issue 2, 2022

Published on: 29 April, 2022

Page: [137 - 146] Pages: 10

DOI: 10.2174/1389202923666220413114659

Price: $65

Abstract

Background: Binning of metagenomic reads is an active area of research, and many unsupervised machine learning-based techniques have been used for taxonomic independent binning of metagenomic reads.

Objective: It is important to find the optimum number of the cluster as well as develop an efficient pipeline for deciphering the complexity of the microbial genome.

Methods: Applying unsupervised clustering techniques for binning requires finding the optimal number of clusters beforehand and is observed to be a difficult task. This paper describes a novel method, MetaConClust, using coverage information for grouping of contigs and automatically finding the optimal number of clusters for binning of metagenomics data using a consensus-based clustering approach. The coverage of contigs in a metagenomics sample has been observed to be directly proportional to the abundance of species in the sample and is used for grouping of data in the first phase by MetaConClust. The Partitioning Around Medoid (PAM) method is used for clustering in the second phase for generating bins with the initial number of clusters determined automatically through a consensus- based method.

Results: Finally, the quality of the obtained bins is tested using silhouette index, rand Index, recall, precision, and accuracy. Performance of MetaConClust is compared with recent methods and tools using benchmarked low complexity simulated and real metagenomic datasets and is found better for unsupervised and comparable for hybrid methods.

Conclusion: This is suggestive of the proposition that the consensus-based clustering approach is a promising method for automatically finding the number of bins for metagenomics data.

Keywords: Binning, consensus clustering, coverage, PAM, unsupervised clustering, metagenomics.

« Previous Next »
Graphical Abstract

[1]
Handelsman, J. Metagenomics: Application of genomics to uncultured microorganisms. Microbiol. Mol. Biol. Rev., 2004, 68(4), 669-685.
[http://dx.doi.org/10.1128/MMBR.68.4.669-685.2004] [PMID: 15590779]
[2]
Meyer, F.; Paarmann, D.; D’Souza, M.; Olson, R.; Glass, E.M.; Kubal, M.; Paczian, T.; Rodriguez, A.; Stevens, R.; Wilke, A.; Wilkening, J.; Edwards, R.A. The metagenomics RAST server - a public resource for the automatic phylogenetic and functional analysis of metagenomes. BMC Bioinformatics, 2008, 9(1), 386-393.
[http://dx.doi.org/10.1186/1471-2105-9-386] [PMID: 18803844]
[3]
Sedlar, K.; Kupkova, K.; Provaznik, I. Bioinformatics strategies for taxonomy independent binning and visualization of sequences in shotgun metagenomics. Comput. Struct. Biotechnol. J., 2016, 15, 48-55.
[http://dx.doi.org/10.1016/j.csbj.2016.11.005] [PMID: 27980708]
[4]
Huson, D.H.; Auch, A.F.; Qi, J.; Schuster, S.C. MEGAN analysis of metagenomic data. Genome Res., 2007, 17(3), 377-386.
[http://dx.doi.org/10.1101/gr.5969107] [PMID: 17255551]
[5]
Segata, N.; Waldron, L.; Ballarini, A.; Narasimhan, V.; Jousson, O.; Huttenhower, C. Metagenomic microbial community profiling using unique clade-specific marker genes. Nat. Methods, 2012, 9(8), 811-814.
[http://dx.doi.org/10.1038/nmeth.2066] [PMID: 22688413]
[6]
Wood, D.E.; Salzberg, S.L. Kraken: Ultrafast metagenomic sequence classification using exact alignments. Genome Biol., 2014, 15(3), R46.
[http://dx.doi.org/10.1186/gb-2014-15-3-r46] [PMID: 24580807]
[7]
Ounit, R.; Wanamaker, S.; Close, T.J.; Lonardi, S. CLARK: Fast and accurate classification of metagenomic and genomic sequences using discriminative k-mers. BMC Genomics, 2015, 16(1), 236.
[http://dx.doi.org/10.1186/s12864-015-1419-2] [PMID: 25879410]
[8]
Qian, J.; Marchiori, D.; Comin, M. Fast and sensitive classification of short metagenomic reads with skraken.Biomedical Engineering Systems and Technologies; Springer, 2017, pp. 212-226.
[9]
Qian, J.; Comin, M. MetaCon: Unsupervised clustering of metagenomic contigs with probabilistic k-mers statistics and coverage. BMC Bioinformatics, 2019, 20(Suppl. 9), 367.
[http://dx.doi.org/10.1186/s12859-019-2904-4] [PMID: 31757198]
[10]
Teeling, H.; Waldmann, J.; Lombardot, T.; Bauer, M.; Glöckner, F.O. TETRA: A web-service and a stand-alone program for the analysis and comparison of tetranucleotide usage patterns in DNA sequences. BMC Bioinformatics, 2004, 5(1), 163-169.
[http://dx.doi.org/10.1186/1471-2105-5-163] [PMID: 15507136]
[11]
Wu, Y.W.; Ye, Y. A novel abundance-based algorithm for binning metagenomic sequences using l-tuples. J. Comput. Biol., 2011, 18(3), 523-534.
[http://dx.doi.org/10.1089/cmb.2010.0245] [PMID: 21385052]
[12]
Chatterji, S.; Yamazaki, I.; Bai, Z.; Eisen, J.A. CompostBin: A DNA composition-based algorithm for binning environmental shotgun reads. arXiv, 2008, 2008, 0708.3098.
[http://dx.doi.org/10.1007/978-3-540-78839-3_3]
[13]
Kislyuk, A.; Bhatnagar, S.; Dushoff, J.; Weitz, J.S. Unsupervised statistical clustering of environmental shotgun sequences. BMC Bioinformatics, 2009, 10(1), 316-331.
[http://dx.doi.org/10.1186/1471-2105-10-316] [PMID: 19799776]
[14]
Kelley, D.R.; Salzberg, S.L. Clustering metagenomic sequences with interpolated Markov models. BMC Bioinformatics, 2010, 11(1), 544-555.
[http://dx.doi.org/10.1186/1471-2105-11-544] [PMID: 21044341]
[15]
Raza, A.; Bardhan, S.; Xu, L.; Yamijala, S.S.; Lian, C.; Kwon, H.; Wong, B.M. A machine learning approach for predicting defluorination of per-and polyfluoroalkyl substances (PFAS) for their efficient treatment and removal. Environ. Sci. Technol. Lett., 2019, 6(10), 624-629.
[http://dx.doi.org/10.1021/acs.estlett.9b00476]
[16]
Perez, H.; Tah, J.H. Improving the accuracy of convolutional neural networks by identifying and removing outlier images in datasets using t-SNE. Mathematics, 2020, 8(5), 662.
[http://dx.doi.org/10.3390/math8050662]
[17]
Lin, H.H.; Liao, Y.C. Accurate binning of metagenomic contigs via automated clustering sequences using information of genomic signatures and marker genes. Sci. Rep., 2016, 6(1), 24175.
[http://dx.doi.org/10.1038/srep24175] [PMID: 27067514]
[18]
Alneberg, J.; Bjarnason, B.S.; de Bruijn, I.; Schirmer, M.; Quick, J.; Ijaz, U.Z.; Quince, C. CONCOCT: Clustering contigs on coverage and composition. Genomics, 2013, 1312, 1-28.
[19]
Wang, Y.; Leung, H.C.; Yiu, S.M.; Chin, F.Y. MetaCluster 5.0: A two-round binning approach for metagenomic data for low-abundance species in a noisy sample. Bioinformatics, 2012, 28(18), i356-i362.
[http://dx.doi.org/10.1093/bioinformatics/bts397] [PMID: 22962452]
[20]
Imelfort, M.; Parks, D.; Woodcroft, B.J.; Dennis, P.; Hugenholtz, P.; Tyson, G.W.; Groop, M.; Groop, M. An automated tool for the recovery of population genomes from related metagenomes. PeerJ, 2014, 2, e603.
[http://dx.doi.org/10.7717/peerj.603] [PMID: 25289188]
[21]
Kang, D.D.; Froula, J.; Egan, R.; Wang, Z. MetaBAT, an efficient tool for accurately reconstructing single genomes from complex microbial communities. PeerJ, 2015, 3, e1165.
[http://dx.doi.org/10.7717/peerj.1165] [PMID: 26336640]
[22]
Lu, Y.Y.; Chen, T.; Fuhrman, J.A.; Sun, F. COCACOLA: Binning metagenomic contigs using sequence COmposition, read CoverAge, CO-alignment and paired-end read LinkAge. Bioinformatics, 2017, 33(6), 791-798.
[PMID: 27256312]
[23]
Xia, X.; Xie, Z. DAMBE: Software package for data analysis in molecular biology and evolution. J. Hered., 2001, 92(4), 371-373.
[http://dx.doi.org/10.1093/jhered/92.4.371] [PMID: 11535656]
[24]
Mende, D.R.; Waller, A.S.; Sunagawa, S.; Järvelin, A.I.; Chan, M.M.; Arumugam, M.; Raes, J.; Bork, P. Assessment of metagenomic assembly using simulated next generation sequencing data. PLoS One, 2012, 7(2), e31386.
[http://dx.doi.org/10.1371/journal.pone.0031386] [PMID: 22384016]
[25]
Sharon, I.; Morowitz, M.J.; Thomas, B.C.; Costello, E.K.; Relman, D.A.; Banfield, J.F. Time series community genomics analysis reveals rapid shifts in bacterial species, strains, and phage during infant gut colonization. Genome Res., 2013, 23(1), 111-120.
[http://dx.doi.org/10.1101/gr.142315.112] [PMID: 22936250]
[26]
Herath, D.; Tang, S.L.; Tandon, K.; Ackland, D.; Halgamuge, S.K. CoMet: A workflow using contig coverage and composition for binning a metagenomic sample with high precision. BMC Bioinformatics, 2017, 18(Suppl. 16), 571.
[http://dx.doi.org/10.1186/s12859-017-1967-3] [PMID: 29297295]
[27]
Gelfand, M.S.; Koonin, E.V. Avoidance of palindromic words in bacterial and archaeal genomes: A close connection with restriction enzymes. Nucleic Acids Res., 1997, 25(12), 2430-2439.
[http://dx.doi.org/10.1093/nar/25.12.2430] [PMID: 9171096]
[28]
Monti, S.; Tamayo, P.; Mesirov, J.; Golub, T. Consensus clustering: A resampling based method for class discovery and visualization of gene expression microarray data. Mach. Learn., 2003, 52(1), 91-118.
[http://dx.doi.org/10.1023/A:1023949509487]

Rights & Permissions Print Cite
Article Metrics
32
1
© 2024 Bentham Science Publishers | Privacy Policy